With the prevalence of mobile data visualizations, there have been growing concerns about their privacy risks, especially shoulder surfing attacks. Inspired by prior research on visual illusion, we propose BAIT, a novel approach to automatically generate privacy-preserving visualizations by stacking a decoy visualization over a given visualization. It allows visualization owners at proximity to clearly discern the original visualization and makes shoulder surfers at a distance be misled by the decoy visualization, by adjusting different visual channels of a decoy visualization (e.g., shape, position, tilt, size, color and spatial frequency). We explicitly model human perception effect at different viewing distances to optimize the decoy visualization design. Privacy-preserving examples and two in-depth user studies demonstrate the effectiveness of BAIT in both controlled lab study and real-world scenarios.
Cite
@article{arxiv.2601.18497,
title = {BAIT: Visual-illusion-inspired Privacy Preservation for Mobile Data Visualization},
author = {Sizhe Cheng and Songheng Zhang and Dong Ma and Yong Wang},
journal= {arXiv preprint arXiv:2601.18497},
year = {2026}
}